SynthesisFrontiers in oncology2026
The value of artificial intelligence in ultrasound imaging for predicting molecular subtypes of breast cancer: a meta-analysis.
Synthesis in Frontiers in oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Ultrasound features and clinicopathologic correlations of breast adenoid cystic carcinoma: a retrospective observational study.Translational cancer research · 2026Article
- Predicting preoperative axillary lymph node metastasis to guide surgical decisions in invasive breast cancer.Frontiers in oncology · 2026Article
- AI-mediated ultrasound radiomics in the diagnosis and treatment of triple-negative breast cancer: research progress and future challenges.Frontiers in oncology · 2026Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The study aims to integrate an evaluation of the accuracy and validity of ultrasonography based artificial intelligence (AI) algorithms for predicting the molecular subtypes of breast cancer patients through a meta-analysis. Methods: A search of the PubMed, Embase, Web of Science, and Cochrane Library databases was performed to locate relevant literature, and the reported studies before February 2026 were included. We evaluated the quality of the studies included by utilizing the Quality Assessment of Diagnostic Accuracy Studies (QUADAS) questionnaire. Two evaluators independently searched for literature and assessed the quality of literature included in the study. Results: A total of thirteen studies assessing a number of 13615 patients of breast cancer were included. The results demonstrated that ultrasonic imaging combined with artificial intelligence algorithm has promising accuracy and effectiveness to predict molecular subtypes of breast cancer. The pooled sensitivity and specificity were 0.89 (95%CI: 0.82-0.93) and 0.82 (95%CI: 0.77-0.86), respectively. Additionally, the diagnostic odds ratios (DOR), positive likelihood ratio (PLR) and negative likelihood ratio (NLR) were 32.10 (95%CI: 18.60-55.38), 4.84 (95%CI:3.80-6.17), and 0.14 (95%CI: 0.09-0.22). The area under the curve (AUC) was 0.91 (95%CI: 0.88-0.93). Publication bias was not significantly observed. Conclusions: Ultrasonic imaging based on artificial intelligence algorithm has good performance and application prospects for forecasting breast cancer molecular subtypes. This technique can help establish the molecular subtype of breast cancer before operation, offering effective help for the treatment plan. It may reduce unnecessary biopsy, which is anticipated to become a meaningful implement in clinical application. Systematic review registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42024599983, identifier CRD42024599983.
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